We present results on image classification benchmarks for long-tail learning:
CIFAR10-LT, CIFAR100-LT, ImageNet-LT and iNaturalist 2018.
Each of these datasets has a skewed training set, and balanced test set.
The long-tailed (“LT”) CIFAR datasets
are constructed by downsampling labels
from the original CIFAR train sets,
following the
Exp profile of [1]}, [2]} with imbalance ratio
\(\rho = {\max _{y} \mathbb {P}( y )} / {\min _{y} \mathbb {P}( y )} = 100\) .
The long-tailed ImageNet dataset is as constructed in [3]},
and iNaturalist as per [4]}.
